Temporal

According to the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Drug Abuse Warning Network (DAWN) surveillance of drug-related ED visits, underage (B21 years) alcohol-alone visit rates have been increasing since 2004 to 2009 (1). Similarly, the ‘‘alcohol’’ syndrome for underage (12-20 years) ED visits also shows an overall increase from 2003 to 2009 in the percentage of alcohol-related visits (2). College-aged drinkers tend to binge drink at a higher frequency than the general population, putting them at greater risk for unintentional injuries and unsafe sex practices (3). Identifying collegespecific patterns for alcohol-associated morbidity have important policy implications to reduce excessive drinking and associated harms on and around college campuses.

Objective

To develop and implement a method for using emergency department records from a syndromic surveillance system to identify alcohol-related visits in New York City, estimate trends, and describe age-specific patterns. In particular, we are interested in college-aged morbidity patterns and how they differ from other age groups.

Visitors from areas outside Miami-Dade County have the potential to introduce diseases and/or strains of microorganisms circulating in their regions of residence. Immunocompromised and immunonaive travelers are at higher risk of contagion by locally transmitted pathogens. The first encounter with a local health care facility for many of these visitors is often an Emergency Departments (ED). Little is known about this group of patients with regard to socio-demographic and temporal patterns. This knowledge is essential to further characterize their syndromic patterns as well as to integrate this knowledge to the growing use of syndromic surveillance as an early-warning public health tool.

Objective

To describe socio-demographic and temporal patterns of patients who reside outside Miami-Dade and who visited EDs of hospitals located in this County during 2007.

Chief complaints are often represented textually and as a mixture of complex and context-dependant lexical symbols with little formal sentence structure. Although human experts usually comprehend this information in its right context intuitively and effortlessly, use of chief complaint data by computers is a challenge. Semantic approaches for text understanding are concerned with the meaning of terms and their relationships, driven from an explicit model rather than their syntactic forms. Explicit representation of domain concepts along with computer reasoning enables a knowledgeable computer agent to identify those concepts in a given text and pinpoint relevant relationships if they make sense according to an existing formal model available to the agent .

Objective

This paper proposes a semantic approach to processing free form text information such as chief complaints using formal knowledge representation and Description Logic reasoning. Our methods extract concepts and as much contextual information as is available in the text. Output consists of a computationally interpretable representation of this information using the Resource Definition Framework (RDF) and UMLS Metathesaurus.

Syndromic surveillance may be suited for detection of emerging respiratory disease elevations that could pass undiagnosed. The syndromes under surveillance should then retrospectively reflect known respiratory pathogen activity. To validate this for respiratory syndromes we analyzed dutch medical registration data from 1999-2003 (national hospital discharge diagnoses and causes of death). We assume that syndromes with a good reflection of pathogen activity have the potential ability to reflect unexpected respiratory pathogen activity in prospective surveillance.

Objective

As a validation for syndromic surveillance we studied whether respiratory syndromes indeed reflect the activity of respiratory pathogens. Therefore we retrospectively estimated the temporal trend of two respiratory syndromes by the seasonal dynamics of common respiratory pathogens.

Temporal anomaly detection is a key component of real time surveillance. Today, despite the abundance of temporal information on multiple syndromes, multivariate investigation of temporal anomalies remains under-explored. Traditionally, an outbreak is thought of as disease localization in time. That is, for an event to qualify as an outbreak, a significant deviation from the observed distribution of the disease must occur.  However, the underlying processes that govern the health seeking behavior of a population with respect to one disease can potentially impact multiple syndromes leading to observable correlation patterns in the daily rates of those syndromes. Thus, a deviation from the observed correlation pattern between different syndromes can be an early indicator of potential anomalies when the rise in the daily rates of one or more syndrome is not sufficiently discernable to be identified by standard univariate techniques.

Objective

The objectives of this study are to develop a mathematical multi-syndrome framework for early detection of temporal anomalies, to demonstrate improvement in detection sensitivity and timeliness of the multivariate technique compared with those of standard uni-syndrome analysis, and to put forward a new practical concept for timely outbreak investigation.

The global health threat of highly pathogenic avian influenza H5N1 has been increasing rapidly in the world since the crosscountry outbreaks during 2003-04. In South and East Asia, the human influenza A (H3N2) was proved to be seeded there with occurring annual cases. Intensive surveillance of influenza is the most urgent strategy to avoid large-scale epidemics and high case fatality rates. Sentinel physicians’ surveillance is the most sensitive mechanism to reflect the health status of community people. In France and Japan, comprehensive sentinel-physician surveillance systems were set up and geographic information system was applied to display the diffusion patterns of influenza-like illness. Kriging method, which was used to display the diffusion, was hard to monitor the multiple temporal and spatial dimensions in one map. Therefore, Ring maps were proposed to overcome this difficulty.

Objective

This study describes a visualizing ring maps to monitor the alert levels of Influenza-like illness, and provide possible insights of temporal and spatial diffusion patterns in epidemic and nonepidemic seasons.

Health care workers (HCWs) have an increased risk of exposure to infectious agents including (among others) tuberculosis, influenza, norovirus, and Clostridium difficile as a consequence of patient care1,2 Most occupational transmission is associated with violation of one or more basic principles of infection control: handwashing; vaccination of HCWs; and prompt isolation.3 OH surveillance is paramount in guiding efforts to improve worker safety and health and to monitor trends and progress over time.4 GIS can assist in supporting health situation analysis and surveillance for the prevention and control of health problems, for example: by creating temporal-spatial maps of outbreaks, public health workers can visualize the spread of cases as the outbreak progresses; spatial/database queries allow for selection of a specific location or condition to focus public health resources.

Objective

This paper describes a GIS tool which maps the floors and departments of a Southeastern Ontario tertiary care hospital for the purpose of monitoring respiratory and gastrointestinal (GI)-related Occupational Health (OH) visits among hospital employees.

Benchmarking of temporal surveillance techniques is a critical step in the development of an effective syndromic surveillance system. Unfortunately, holding “bakeoffs” to blindly compare approaches is a difficult and often fruitless enterprise, in part due to the parameters left to the final user for tuning. In this paper, we demonstrate how common analytical development and analysis may be coupled with realistic data sets to provide insight and robustness when selecting a surveillance technique.

OBJECTIVE

This paper compares the robustness and performance of three temporal surveillance techniques using a twofold approach: 1) a unifying statistical analysis to establish their common features and differences, and 2) a benchmarking on respiratory, influenza-like ill-nesses, upper GI, and lower GI complaint time series from the Harvard Pilgrim Health Care (HPHC).

Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC is a Bayesian network disease outbreak detection system that models 12 diseases. A system that looks only at each day's data might signal an outbreak one day and not signal it the next. Cooper et al. obtained such results when evaluating the ability of PC to detect a laboratory validated outbreak of influenza. We hypothesized that temporal modeling would attenuate this problem.

Objective

A temporal method for outbreak detection using a Bayesian network is presented and evaluated.